Hybrid Segmentation Research Articles

Closing the Perception Gap in Advertising Design

This study examines how to increase the persuasiveness of advertisements by eliminating the disparities between marketer perceptions of the product and consumer cognition regarding the product. A means-end hybrid segmentation model (MHSM) is proposed based on means-end chain (MEC) theory and a multi-segmentation approach to deduce the literal contents of advertising design for a homogenous consumption group. Contextual segmentation variables were combined with attribute/benefit/value (A/B/V) preference hierarchies to formulate the literal contents of advertising design. The analytical results indicate that MHSM can simultaneously explore consumer product cognitive hierarchies of different product category selections in different consumption situations.

Localization and Segmentation of Left Ventricle in Cardiac Cine-MR Images

Accurate delineation of the left ventricular myocardial boundaries on cardiac cine magnetic resonance (MR) images is essential for volumetric and functional cardiac analysis. Automated myocardial contour delineation often suffers from misalignment of slices, nonuniform coil sensitivity, blood-flow-related inter- and intraslice intensity inhomogeneities, blurring due to motion, partial voluming, and a need to circumscribe the papillary muscles and the trabeculae. In this paper, we propose a novel method for data-driven localization and segmentation of the left ventricle in the cine-MR images toward automated computation of ejection fraction (EF). Our hybrid segmentation method combines intensity- and texture-based fuzzy affinity maps obtained from a novel multiclass, multifeature fuzzy connectedness method with dynamic-programming-based boundary detection to delineate the myocardial contours. Bland-Altman analysis indicates that the mean biases of the end-diastolic volume, end-systolic volume, and EF estimates of our method are comparable to the interobserver variability when compared with the annotations from two experts.

Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: Method and validation

The segmentation from MRI of macroscopically ill-defined and highly variable structures, such as the hippocampus (Hc) and the amygdala (Am), requires the use of specific constraints. Here, we describe and evaluate a fast fully automatic hybrid segmentation that uses knowledge derived from probabilistic atlases and anatomical landmarks, adapted from a semi-automatic method. The algorithm was designed at the outset for application on images from healthy subjects and patients with hippocampal sclerosis. Probabilistic atlases were built from 16 healthy subjects, registered using SPM5. Local mismatch in the atlas registration step was automatically detected and corrected. Quantitative evaluation with respect to manual segmentations was performed on the 16 young subjects, with a leave-one-out strategy, a mixed cohort of 8 controls and 15 patients with epilepsy with variable degrees of hippocampal sclerosis, and 8 healthy subjects acquired on a 3 T scanner. Seven performance indices were computed, among which error on volumes RV and Dice overlap K. The method proved to be fast, robust and accurate. For Hc, results with the new method were: 16 young subjects {RV=5%, K=87%}; mixed cohort {RV=8%, K=84%}; 3 T cohort {RV=9%, K=85%}. Results were better than with atlas-based (thresholded probability map) or semi-automatic segmentations. Atlas mismatch detection and correction proved efficient for the most sclerotic Hc. For Am, results were: 16 young controls {RV=7%, K=85%}; mixed cohort {RV=19%, K=78%}; 3 T cohort {RV=10%, K=77%}. Results were better than with the semi-automatic segmentation, and were also better than atlas-based segmentations for the 16 young subjects.

Robust Detection of Phone Boundaries Using Model Selection Criteria With Few Observations

Automatic phone segmentation techniques based on model selection criteria are studied. We investigate the phone boundary detection efficiency of entropy- and Bayesian- based model selection criteria in continuous speech based on the DISTBIC hybrid segmentation algorithm. DISTBIC is a text-independent bottom-up approach that identifies sequential model changes by combining metric distances with statistical hypothesis testing. Using robust statistics and small sample corrections in the baseline DISTBIC algorithm, phone boundary detection accuracy is significantly improved, while false alarms are reduced. We also demonstrate further improvement in phonemic segmentation by taking into account how the model parameters are related in the probability density functions of the underlying hypotheses as well as in the model selection via the information complexity criterion and by employing M-estimators of the model parameters. The proposed DISTBIC variants are tested on the NTIMIT database and the achieved F 1 measure is 74.7% using a 20-ms tolerance in phonemic segmentation.

Segmentation of Pulmonary Vascular Trees from Thoracic 3D CT Images.

This paper describes an algorithm for extracting pulmonary vascular trees (arteries plus veins) from three-dimensional (3D) thoracic computed tomographic (CT) images. The algorithm integrates tube enhancement filter and traversal approaches which are based on eigenvalues and eigenvectors of a Hessian matrix to extract thin peripheral segments as well as thick vessels close to the lung hilum. The resultant algorithm was applied to a simulation data set and 44 scans from 22 human subjects imaged via multidetector-row CT (MDCT) during breath holds at 85% and 20% of their vital capacity. A quantitative validation was performed with more than 1000 manually identified points selected from inside the vessel segments to assess true positives (TPs) and 1000 points randomly placed outside of the vessels to evaluate false positives (FPs) in each case. On average, for both the high and low volume lung images, 99% of the points was properly marked as vessel and 1% of the points were assessed as FPs. Our hybrid segmentation algorithm provides a highly reliable method of segmenting the combined pulmonary venous and arterial trees which in turn will serve as a critical starting point for further quantitative analysis tasks and aid in our overall goal of establishing a normative atlas of the human lung.

Hybrid Segmentation – Artificial Neural Network Classification of High Resolution Hyperspectral Imagery for Site-Specific Herbicide Management in Agriculture

Site-Specific Herbicide Management (SSHM) in Precision Agriculture (PA) requires weed detection in crop fields for directed herbicide application instead of spraying entire fields. This has significant economic and environmental advantages for improved agricultural practices that are essential given forecast increases in global population and food production needs. In this study, a new hybrid segmentation - Artificial Neural Network (HS-ANN) method was compared to standard Maximum Likelihood Classification (MLC) for improving crop/weed species discrimination in SSHM/PA. Very high spatial resolution (1.25 mm) ground-based hyperspectral image data were acquired over field plots of canola, pea, and wheat crops seeded with two weed species (redroot pigweed, wild oat) in southern Alberta, Canada. The very high spatial and spectral resolution image data required development of a simple yet efficient vegetation index (Modified Chlorophyll Absorption in Reflectance Index (MCARI)) threshold segmentation to separate vegetation from soil for classification. The HSANN consistently outperformed MLC in both single date and multi-temporal classifications. Higher class accuracies were obtained with multi-temporally trained ANNs (84 to 92 percent overall), with improvements up to 31 percent over MLC. With advancements in imaging technology and computer processing speed, this HS-ANN method may constitute a viable farm option for real-time detection and mapping of weed species for SSHM in agriculture.

Detection of constituent layers of histological oral sub-mucous fibrosis: Images using the hybrid segmentation algorithm

This paper concentrates on the segmentation of histological images of oral sub-mucous fibrosis (OSF) into its constituent layers. In this regard hybrid segmentation algorithm shows very interesting results. The segmentation results depict the superiority of hybrid segmentation algorithm (HSA) in comparison to region growing algorithm (RGA). In clinical sense, the presented method provides an automatic means for segmenting histological layers (reference class map provided by the expert). The method shows potential in mimicking clinical acumen to act as a support system to oncologist.

A Compact and Accurate Gaussian Variate Generator

A compact, fast, and accurate realization of a digital Gaussian variate generator (GVG) based on the Box-Muller algorithm is presented. The proposed GVG has a faster Gaussian sample generation rate and higher tail accuracy with a lower hardware cost than published designs. The GVG design can be readily configured to achieve arbitrary tail accuracy (i.e., with a proposed 16-bit datapath up to plusmn15 times the standard deviation sigma) with only small variations in hardware utilization, and without degrading the output sample rate. Polynomial curve fitting is utilized along with a hybrid (i.e., combination of logarithmic and uniform) segmentation and a scaling scheme to maintain accuracy. A typical instantiation of the proposed GVG occupies only 534 configurable slices, two on-chip block memories, and three dedicated multipliers of the Xilinx Virtex-II XC2V4000-6 field-programmable gate array (FPGA) and operates at 248 MHz, generating 496 million Gaussian variates (GVs) per second within a range of plusmn6.66sigma. To accurately achieve a range of plusmn9.4sigma, the GVG uses 852 configurable slices, three block memories, and three on-chip dedicated multipliers of the same FPGA while still operating at 248 MHz, generating 496 million GVs per second. The core area and performance of a GVG implemented in a 90-nm CMOS technology are also given. The statistical characteristics of the GVG are evaluated and confirmed using multiple standard statistical goodness-of-fit tests.

User-driven page layout analysis of historical printed books

In this paper, based on the study of the specificity of historical printed books, we first explain the main error sources in classical methods used for page layout analysis. We show that each method (bottom-up and top-down) provides different types of useful information that should not be ignored, if we want to obtain both a generic method and good segmentation results. Next, we propose to use a hybrid segmentation algorithm that builds two maps: a shape map that focuses on connected components and a background map, which provides information about white areas corresponding to block separations in the page. Using this first segmentation, a classification of the extracted blocks can be achieved according to scenarios produced by the user. These scenarios are defined very simply during an interactive stage. The user is able to make processing sequences adapted to the different kinds of images he is likely to meet and according to the user needs. The proposed “user-driven approach” is capable of doing segmentation and labelling of the required user high level concepts efficiently and has achieved above 93% accurate results over different data sets tested. User feedbacks and experimental results demonstrate the effectiveness and usability of our framework mainly because the extraction rules can be defined without difficulty and parameters are not sensitive to page layout variation.

Automatic Seed Generation Using Discrete Cosine Transform for 2D Region Growing Segmentation of Computed Tomography Image Sequence - A New Hybrid Segmentation Technique

Automatic Seed Generation Using Discrete Cosine Transform for 2D Region Growing Segmentation of Computed Tomography Image Sequence - A New Hybrid Segmentation Technique

Novel Multistage Three-Dimensional Medical Image Segmentation: Methodology and Validation

In this paper, we propose a novel multistage method for three-dimensional (3-D) segmentation of medical images and a new radial distance-based segmentation validation approach. For the 3-D segmentation method, we first employ a morphological recursive erosion operation to reduce the connectivity between the region of interest and its surrounding neighborhood; then we design a hybrid segmentation method to achieve an initial result. The hybrid approach integrates an improved fast marching method and a morphological reconstruction algorithm. Finally, a morphological recursive dilation is employed to recover any lost structure from the first stage of the multistage method. This approach is tested on 12 CT and 3 MRI images of the brain, heart, and kidney, to demonstrate the effectiveness and accuracy of this technique across a variety of imaging modalities and organ systems. In order to validate the multistage segmentation method, a novel radial distance-based validation method is proposed that uses a global accuracy (GA) measure. The GA is calculated based on local radial distance errors (LRDE), where LRDE are calculated on the radii emitted from points along the skeleton of the object rather than the centroid, in order to accommodate more complicated organ structures. The experimental results demonstrate that the proposed multistage segmentation method is fast and accurate, with comparable performance to existing segmentation methods, but with a significantly higher execution speed.

Hybrid segmentation of colon filled with air and opacified fluid for CT colonography

Reliable segmentation of the colon is a requirement for three-dimensional visualization programs and automatic detection of polyps on computed tomography (CT) colonography. There is an evolving clinical consensus that giving patients positive oral contrast to tag out remnants of stool and residual fluids is mandatory. The presence of positive oral contrast in the colon adds an additional challenge for colonic segmentation but ultimately is beneficial to the patient because the enhanced fluid helps reveal polyps in otherwise hidden areas. Therefore, we developed a new segmentation procedure which can handle both air- and fluid-filled parts of the colon. The procedure organizes individual air- and fluid-filled regions into a graph that enables identification and removal of undesired leakage outside the colon. In addition, the procedure provides a risk assessment of possible leakage to assist the user prior to the tedious task of visual verification. The proposed hybrid algorithm uses modified region growing, fuzzy connectedness and level set segmentation. We tested our algorithm on 160 CT colonography scans containing 183 known polyps. All 183 polyps were in segmented regions. In addition, visual inspection of 24 CT colonography scans demonstrated good performance of our procedure: the reconstructed colonic wall appeared smooth even at the interface between air and fluid and there were no leaked regions.

Ground truth for evaluation of ischemic stroke hybrid segmentation in a rat model of temporary middle cerebral artery occlusion

In vivo rodent models of focal cerebral ischemia have been developed to investigate stroke therapy. Typically these models require rapid quantification of cerebral infarct volumes using vital stains with tetrazolium salts to delineate the extent of neuronal death. To avoid animal sacrifice, we sought a study with MR acquired volumetric rat data where surrogate of ground truth is obtained by repeated manual delineation by experts, and an automated hybrid segmentation is evaluated for accuracy. We propose a rating system for the expert delineations that captures intra- and inter-expert discrepancy. Our preliminary results show that surrogate ground truth derived from MR data is at least as good as the one derived from histologic stained slices. Hence animal sacrifice is not necessary to evaluate ischemic stroke automated segmentation in a rat model of temporary middle cerebral artery occlusion.

Linking global market segmentation decisions with strategic positioning options

PurposeThe purpose of this paper is to examine empirically the relationship between positioning strategies and bases of segmentation in international markets.Design/methodology/approachA principal component analysis was conducted to determine the major macro‐ as well as micro‐bases of segmentation that are linked with strategic positioning decision options. Further, a regression analysis was used to examine the effect of each of the segmentation bases on the different strategic positioning options used by segmentation managers.FindingsThis study suggests the combined use of both macro‐ and micro‐bases of segmentation in order to leverage similar strategic positioning across global markets. However, micro‐bases of segmentation are suggested for firms seeking differential positioning strategies.Research limitations/implicationsThe conceptual and empirical findings of this study pave the way for embarking on promising and relevant future research that is needed to substantiate and enrich the academic understanding and managerial practice of linking global segmentation with strategic positioning decisions. Future research should focus on the use of hybrid segmentation strategies; its logical design; implementation issues; and its evaluation mechanism.Practical implicationsThis study provides specific empirical evidence of the relationship between strategic use of segmentation bases and strategic positioning. An effective use of the proposed framework will have various strategic marketing implications for firms; including cost efficiencies, opportunities to transfer products globally, expansion opportunities of current operation, and development of more effective brand management decisions.Originality/valueThe proposed global strategic segmentation and positioning matrix is a new tool that guides managers to position their brands effectively in world markets.

Automatic segmentation of diatom images for classification

A general framework for automatic segmentation of diatom images is presented. This segmentation is a critical first step in contour-based methods for automatic identification of diatoms by computerized image analysis. We review existing results, adapt popular segmentation methods to this difficult problem, and finally develop a method that substantially improves existing results. This method is based on the watershed segmentation from mathematical morphology, and belongs to the class of hybrid segmentation techniques. The novelty of the method is the use of connected operators for the computation and selection of markers, a critical ingredient in the watershed method to avoid over-segmentation. All methods considered were used to extract binary contours from a large database of diatom images, and the quality of the contours was evaluated both visually and based on identification performance.

Hybrid segmentation and virtual bronchoscopy based on CT images 1

Rationale and objectives Introduction of combination of the segmentation tool SegoMeTex and the virtual endoscopy system VIVENDI to perform virtual endoscopic inspections of the human lung. This virtual bronchoscopy system enables visualization of the tracheobronchial tree down to seventh generation. Furthermore, the modified virtual system visualizes hidden structures such as segmented vascular system or tumors. Materials and methods The segmentation is based on image data acquired by a multislice computed tomography scanner. SegoMeTex is used to segment the tracheobronchial tree by a hybrid system with minimal user action. Similarly, the complementary pulmonary arterial can be segmented, whereas additional structures such as tumors are marked manually. On this dataset, s subsequently, data structures of the inner surface for virtual endoscopy are generated. Finally, the dataset can be explored by a virtual bronchoscopy procedure using the VIVENDI system. Results The segmentation method was successfully tested on 22 patients. The hybrid segmentation system identified bronchi up to the sixth generation with a sensitivity of more than 58%, and a positive predictive value of more than 90%. After the segmentation, the datasets are explored interactively (>30 fps on a standard personal computer platform in real-time rendering) using the virtual endoscopy software. The exploration exposed a high-quality reconstruction, even of small structures throughout the dataset. Conclusion Virtual bronchoscopy in combining with a highly sensitive segmentation is a valuable tool for the localization and measurement of stenosis for resection planning.

Markov random field modeled range image segmentation

In this paper range image segmentation is cast in the framework of Bayes inference and Markov random field modeling. To facilitate the inference from distance measurement to labeling set, we introduce the set of surface function parameters as another estimation and construct a novel model accordingly. Subsequent study shows that range image segmentation can be formulated as a combinatorial optimization problem. This model-based optimization will be used as a postprocessing module after an edge-based initial segmentation in our hybrid segmentation scheme. Qualitative improvement is observed from experimental results.

Segmentation and virtual exploration of tracheobronchial trees

The tracheobronchial tree as part of the lung is part of one of the most important organs of the human body. Inhaled air is distributed to the alveolus where oxygen and carbon dioxide exchange between air and blood takes place. In this paper, we introduce the virtual endoscopy system VIVENDI to perform virtual inspections of tracheobronchial trees based on their segmentation and of the complementing blood vessels. It is based on a hybrid segmentation pipeline which enables the segmentation of vascular and tracheobronchial structures down to the seventh generation of the bronchi.

Handbook of Medical Imaging. Processing and Analysis

I Enhancement Fundametal Enhancement Techniques Adaptive Image Filtering Enhancement by Multiscale Nonlinear Operators Medical Image Enhancement with Hybrid Filters. II Segmentation Overview and Fundamentals of Medical Image Segmentation Image Segmentation by Fuzzy Clustering: Methods and Issues Segmentation with Neural Networks Deformable Models Shape Constraints in Deformable Models Gradient Vector Flow Deformable Models Fully Automated Hybrid Segmentation of the Brain Volumetric Segmentation Partial Volume Segmentation with Voxel Histograms. III Quantification Two-dimensional Shape and Texture Quantification Texture Analysis in Three Dimensions as a Cue to Medical Diagnosis Computational Neuroanatomy Using Shape Transformation Morphometry of Arterial Trees and Microvascular Networks Image-Based Computational Biomechanics of the Musculoskeletal System Three-Dimensional Bone Angle Quantification Database Selection and Feature Extraction for Neural Networks Quantitative Image Analysis for Estimation of Breast Cancer Risk Classification of Breast Lesions in Mammograms Quantitative Analysis of Cardiac Function Image Processing and Analysis in Tagged Cardiac MRI Image Interpolation and Resampling. IV Registration The Physical Basis of Spatial Distortions in Magnetic Resonance Images Physical and Biological Bases of Spatial Distortiobns in Positron Emission Tomography Images Biological Underpinnings of Anatomic Consistency and Variability in the Human Brain Spatial Transformation Models Validation of Registration Accuracy Landmark-based Registration using Features Identified Through Differential Geometry Registration of Contours using Chamfer Matching Within-Modality Registration using Intensity-Based Cost Functions Across-Modality Registration using Intensity-Based Cost Functions Talairach Space as a Tool for Intersubject Standardization in the Brain Warping Strategies for Intersubject Registration Optimizing the Resampling of Registered Images Clinical Applications of Image Registration Registration for Image-Guided Surgery Image Registration and the Construction of Multidimensional Brain Atlases. V Visualization Visualization Pathways in Biomedicine Three-Dimensional Visualization in Medicine and Biology Volume Visualization in Medicine Fast Isosurface Extraction Methods for Large Image Data Sets Morphometric Methods for Virtual Endoscopy. VI Compression Storage and Communication Fundamentals and Standards of Compression and Communication Medical Image Archive and Retrieval Image Standardization in PACS Quality Evaluation for Compressed Medical Images: Fundamentals Quality Evaluation for Compressed Medical Images: Diagnostic Accuracy Quality Evaluation for Compressed Medical Images: Statistical Issues Three-Dimensional Image Compression with Wavelet Transforms Medical Image Processing and Analysis Software. Index

Integrating Contextual Information with per-Pixel Classification for Improved Land Cover Classification

A hybrid segmentation procedure to integrate contextual information with per-pixel classification in a metropolitan area land cover classification project is described and evaluated. It is presented as a flexible tool within a commercially available image processing environment, allowing components to be adapted or replaced according to the user's needs, the image type, and the availability of state-of-the-art algorithms. In the case of the Twin Cities metropolitan area of Minnesota, the combination of the Shen and Castan edge detection operator with iterative centroid linkage region growing/merging based on Student's t-tests proved optimal when compared to other more common contextual approaches, such as majority filtering and the Extraction and Classification of Homogeneous Objects classifier. Postclassification sorting further improved the results by reducing residual confusion between urban and bare soil categories. Overall accuracy of the optimal classification technique was 91.4% for a level II classification (10 classes) with a K e of 90.5%. The incorporation of contextual information in the classification process improved accuracy by 5.8% and K e by 6.5%. As expected, classification accuracy for a simplified level I classification (five classes) was higher with 95.4% and 94.3% for K e . A second important advantage of the technique is the reduced occurrence of smaller mapping units, resulting in a more attractive classification map compared to traditional per-pixel maximum likelihood classification results.